FIGURE 1. Visualization of part of the alignment of proteins with ‘Gemini_BL1’ domain made by MUSCLE algorithm in MEGA.
In order to evaluate the efficiency of DMPfold2, I executed the program changing the number of iteration cycles and the number of steps in the geometry minimization. The program used 24 threads, half of the total number of threads (Intel(R) Xeon(R) Silver 4214 CPU @ 2.20GHz).
However, if the MSA is submitted to the 3) trRosetta server, the job is completed in less than 2.5 hours. The problem would be that the confidence of the predicted model is low (estimated TM-score=0.245; 0.5 usually indicates a model with correctly predicted topology.)[1]. The resulting predicted proteins structures were visualized by UCSF Chimera.
SOME NOTES ABOUT STRUCTURE EVALUATION
In terms of quality, the models generated by DMPfold2 were globally not as good as the model obtained with the algorithm of trRosetta, as we can observe in the different outputs of programs for the evaluation of structural protein models (Figures 2-4 and Tables 1-3).
From my point of view, it would be worth to try Alphafold2, due to its accuracy in predicting protein structures (it is open source now) and in order to compare its results using this MSA.
TABLE 1. Summary statistics of the geometrical features when MSA of the proteins of the ‘Gemini_BL1’ domain family is given to DMPfold2 with 1000 iterations and 1000 minimization steps. The statistics were calculated by MolProbity software [2].
FIGURE 2. Results of evaluation of the structural model: A) local model quality, B) overall model quality [3] and C) Ramachandran plots [2].
TABLE 2. Summary statistics of the geometrical features when MSA of the proteins of the ‘Gemini_BL1’ domain family is given to DMPfold2 with 10000 iterations and 1000 minimization steps. The statistics were calculated by MolProbity software [2].
FIGURE 3. Results of evaluation of the structural model: A) local model quality, B) overall model quality [3] and C) Ramachandran plots [2].